Paper
25 July 2001 Overview of fluid-based quick simulation techniques for large packet-switched communication networks
Nenad Milidrag, George Kesidis, Mihail Devetsikiotis
Author Affiliations +
Proceedings Volume 4526, Scalability and Traffic Control in IP Networks; (2001) https://doi.org/10.1117/12.434403
Event: ITCom 2001: International Symposium on the Convergence of IT and Communications, 2001, Denver, CO, United States
Abstract
We consider a large packet-switched communication network. Traffic in such networks is heavily aggregated especially in the network core. Fluid traffic models have been used for this reason and because the individual packets are very small compared to the volume of aggregated traffic. Fluid models have also been considered for the network components themselves in order to explore the possibility of simulation speed-up. In the event-driven simulation of Kesidis et al of such a `fluid' network, a `ripple effect' was described to explain the substantial degradation in simulation speed-up as the network size grew, especially when work-conserving bandwidth schedulers were present. Thereafter, studies attempted to identify under what network dimensions and designs and under what traffic conditions the ripple effect is minimized. Hybrids of packet/fluid and event/time-driven simulation strategies were considered. This paper gives an overview of the fluid network modeling approach and surveys recent work on such hybrid approaches.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Nenad Milidrag, George Kesidis, and Mihail Devetsikiotis "Overview of fluid-based quick simulation techniques for large packet-switched communication networks", Proc. SPIE 4526, Scalability and Traffic Control in IP Networks, (25 July 2001); https://doi.org/10.1117/12.434403
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Cited by 16 scholarly publications.
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KEYWORDS
Computer simulations

Fluid dynamics

Error analysis

Global Positioning System

Network architectures

Data modeling

Stochastic processes

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